Server, control device for vehicle, and machine learning system for vehicle

ABSTRACT

A server including a processor configured to: receive a data set from a vehicle; create a plurality of learned models of different scales by performing machine learning using the data set; receive from the vehicle information on computing power of an electronic control unit that controls the vehicle by applying the learned model; and transmit the learned model to the vehicle, wherein the processor is configured to transmit the learned model of a larger scale to the vehicle equipped with the electronic control device having high computing power, than to the vehicle equipped with the electronic control device having low computing power.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Japanese Patent Application No.2020-134806 filed on Aug. 7, 2020, incorporated herein by reference inits entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to a server, a control device for avehicle, and a machine learning system for a vehicle.

2. Description of Related Art

There is known a technology in which data acquired in a vehicle isreceived by a communication unit of a server, a data set is createdusing the received data, learning is performed according to the dataset, and the learned model is transmitted from the server to the vehicle(see, for example, Japanese Unexamined Patent Application PublicationNo. 2019-183698 (JP 2019-183698 A)).

SUMMARY

However, the types of vehicles that receive the learned model from theserver vary, and the computing power of an in-vehicle computer that eachvehicle is equipped with also varies. When the server uniformly providesthe learned model to each vehicle without considering the computingpower of the in-vehicle computer, there is a possibility that thelearned model cannot be used in a vehicle equipped with an in-vehiclecomputer having a relatively low computing power.

In view of the above issue, an object of the present disclosure is toprovide a server, a control device for a vehicle, and a machine learningsystem for a vehicle that enable the use of a learned model inaccordance with the computing power of the control device provided inthe vehicle.

The gist of the present disclosure is as follows.

An aspect of the present disclosure relates to a server including aprocessor configured to: receive a data set from a vehicle; create aplurality of learned models of different scales by performing machinelearning using the data set; receive, from the vehicle, information oncomputing power of an electronic control unit that controls the vehicleby applying the learned model; and transmit the learned model to thevehicle, wherein the processor is configured to transmit the learnedmodel of a larger scale to the vehicle equipped with the electroniccontrol unit having high computing power, than to the vehicle equippedwith the electronic control unit having low computing power.

In the above aspect, the processor may be configured to perform themachine learning by a neural network.

In the above aspect, the scale of the learned model may be larger as thenumber of hidden layers or the number of nodes existing in the hiddenlayers increases.

In the above aspect, the processor may be configured to: create thelearned model for each region in which the vehicle travels, the learnedmodel being of a larger scale as the region is larger; and transmit thelearned model corresponding to the region in which the vehicle currentlytravels based on position information of the vehicle.

In the above aspect, the processor may be configured to transmit thelearned model at a higher transmission frequency to the vehicle equippedwith the electronic control unit having low computing power, than to thevehicle equipped with the electronic control unit having high computingpower.

Another aspect of the present disclosure relates to a control device ofa vehicle that controls the vehicle by applying a learned model, thecontrol device including a processor configured to: transmit informationon computing power of the control device to a server; receive thelearned model of a scale corresponding to the computing power from theserver; and control the vehicle by applying the learned model of thescale corresponding to the computing power, the learned model beingreceived from the server.

In the above aspect, the processor may be configured to: transmit a dataset to the server; and receive the learned model created by the serverby performing machine learning using the data set.

In the above aspect, the server may perform the machine learning by aneural network; and the scale of the learned model may be larger as thenumber of hidden layers or the number of nodes existing in the hiddenlayers increases.

In the above aspect, the processor may be configured to: receive thelearned model corresponding to a region in which the vehicle currentlytravels for each region in which the vehicle travels; and receive thelearned model of a larger scale as the region is larger.

In the above aspect, the processor may be configured to receive thelearned model at a higher reception frequency as the computing power islower.

Another aspect of the present disclosure relates to a machine learningsystem for a vehicle, the machine learning system including: a serverthat creates a learned model by performing learning using a data setreceived from the vehicle; and a control device for the vehicle forcontrolling the vehicle by applying the learned model, wherein: thecontrol device includes a first processor configured to: transmit thedata set to the server; transmit information on computing power of thecontrol device to the server; receive the learned model of a scalecorresponding to the computing power from the server; and control thevehicle by applying the learned model of the scale corresponding to thecomputing power, the learned model being received from the server; andthe server includes a second processor configured to: receive the dataset from the vehicle; create a plurality of learned models of differentscales by performing machine learning using the data set; receiveinformation on the computing power of the control device from thevehicle; transmit the learned model to the vehicle; and transmit thelearned model of a larger scale to the vehicle equipped with the controldevice having high computing power, than to the vehicle equipped withthe control device having low computing power.

In the above aspect, the second processor may be configured to performthe machine learning by a neural network.

In the above aspect, the scale of the learned model may be larger as thenumber of hidden layers or the number of nodes existing in the hiddenlayers increases.

In the above aspect, the second processor may be configured to: createthe learned model for each region in which the vehicle travels; createthe learned model of a larger scale as the region is larger; andtransmit the learned model corresponding to the region in which thevehicle currently travels based on position information of the vehicle;and the first processor may be configured to receive the learned modelcorresponding to the region in which the vehicle currently travels foreach region in which the vehicle travels.

In the above aspect, the second processor may be configured to transmitthe learned model at a higher transmission frequency to the vehicleequipped with the control device having low computing power, than to thevehicle equipped with the control device having high computing power;and the first processor may be configured to receive the learned modelat a higher reception frequency as the computing power is lower.

According to the present disclosure, it is possible to provide thelearned model to the vehicle according to the computing power of thecontrol device included in the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, advantages, and technical and industrial significance ofexemplary embodiments of the disclosure will be described below withreference to the accompanying drawings, in which like signs denote likeelements, and wherein:

FIG. 1 is a schematic diagram showing a configuration of a machinelearning system for a vehicle according to an embodiment;

FIG. 2 is a schematic diagram showing a configuration of a vehiclecontrol system mounted on the vehicle;

FIG. 3 is a schematic diagram showing a configuration of a server;

FIG. 4 is a schematic diagram showing both functional blocks of aprocessor of an electronic control unit (ECU) provided in the vehicleand functional blocks of a processor of a control device provided in theserver;

FIG. 5 is a schematic diagram showing a map defining in advance therelationship between computing power of the ECU and the scale of alearned model;

FIG. 6 is a schematic diagram showing a table used by a model scaledetermination unit to obtain the computing power from specificationinformation of the ECU;

FIG. 7 is a schematic diagram showing an example of a neural networkused by a learning unit that performs learning based on a data set tocreate the learned model;

FIG. 8 is a sequence diagram showing processes performed by the ECUprovided in the vehicle and the control device provided in the server;

FIG. 9 is a schematic diagram showing an example in which learned modelsof different scales are associated with regions according to the size ofthe regions; and

FIG. 10 is a schematic diagram showing an example in which learnedmodels of different scales are associated with regions according to thesize of the regions.

DETAILED DESCRIPTION

Hereinafter, several embodiments according to the present disclosurewill be described with reference to the drawings. However, thesedescriptions are intended merely to illustrate embodiments of thepresent disclosure and are not intended to limit the present disclosureto such particular embodiments.

FIG. 1 is a schematic diagram showing a configuration of a machinelearning system 1000 for a vehicle according to an embodiment. Themachine learning system 1000 has a plurality of vehicles 100 and aserver 200. The vehicles 100 are manually driven vehicles that aremanually driven by a driver, autonomous vehicles that can travelautonomously, and the like. Each vehicle 100 and the server 200 cancommunicate with each other via a communication network 300 composed ofan optical communication line or the like and a radio base station 400connected to the communication network 300 via a gateway (not shown).That is, the radio base station 400 relays the communication betweeneach vehicle 100 and the server 200.

Each vehicle 100 collects various types of information such as an imagetaken by an in-vehicle camera, information representing a driving state,and information representing the environment around the vehicle, createslearning data sets from the information, and transmits the data sets tothe server 200. The server 200 is provided in a management center. Themanagement center causes the server 200 to learn the data sets, andprovides the learned models obtained as a result of the learning to thevehicles 100 by using the server 200. Learning on the server 200 isperformed by machine learning such as deep learning. In addition tothis, the management center can perform various processes forcontrolling each vehicle 100 by using the server 200.

The server 200 performs learning based on the data sets acquired fromthe vehicles 100, and provides the learned models to each vehicle 100.Alternatively, the server 200 may perform learning based on the datasets acquired from a specific vehicle 100, and provide the specificvehicle 100 with a learned model specialized for the specific vehicle100. Further, the server 200 may provide a vehicle 100 that has nottransmitted the learning data set to the server 200 with a learned modelcreated based on a data set acquired from another vehicle 100.

The performances of the vehicles 100 that communicate with the server200 vary. An in-vehicle computer provided in each of the vehicles 100differs depending on the performance of the vehicle 100. The higher theperformance of the vehicle 100 is, the more sophisticated and precisevehicle control tends to be performed, so that an in-vehicle computerhaving higher computing power tends to be mounted.

As for the learned model provided from the server 200 to the vehicle100, the larger the scale of the learned model is, the more complicatedthe model is, which is suitable for more sophisticated and precisevehicle control. The scale of the learned model is a value correspondingto the computing power (computing load) required to use the learnedmodel. When the learned model is composed of a neural network, the scaleof the learned model is represented by the number of hidden layers andthe number of nodes existing in the hidden layers. The larger the numberof hidden layers and the number of nodes existing in the hidden layersare, the larger the scale of the learned model is. As an example, thescale of the learned model is calculated by multiplying the averagenumber of nodes existing per hidden layer by the number of hiddenlayers. As the number of hidden layers and the number of nodes in thehidden layers in the learned model increase, the number of weightingcoefficients and the number of biases determined by learning increase,resulting in a model that more accurately represents the actual state.Therefore, a learned model of a larger scale is more suitable for moresophisticated and precise vehicle control. The scale of the learnedmodel is also represented by the number of weighting coefficients andthe number of biases determined by learning. The larger the number ofweighting coefficients and the number of biases are, the larger thescale of the learned model is.

When the scale of the learned model exceeds the computing power of thein-vehicle computer, there is a possibility that the in-vehicle computercannot perform calculations on the large-scaled and complicated learnedmodel, and vehicle control by applying the learned model cannot beperformed. Further, when the scale of the learned model is too small forthe computing power of the in-vehicle computer, the accuracy of vehiclecontrol by applying the learned model may decrease. Therefore, thevehicle 100 may be provided with a learned model suitable for thecomputing power in accordance with the computing power of the in-vehiclecomputer of the individual vehicle 100.

In the present embodiment, specification information indicating thecomputing power of the in-vehicle computer is transmitted from thevehicle 100 to the server 200. Based on the specification information,the server 200 transmits a learned model of a scale corresponding to thecomputing power of the in-vehicle computer to the vehicle 100. Morespecifically, the server 200 transmits a learned model of a larger scalewhen the computing power of the in-vehicle computer is high than whenthe computing power is low. This avoids the situation where vehiclecontrol by applying the learned model cannot be performed due to thelack of the computing power of the in-vehicle computer. Further, vehiclecontrol by applying the learned model is performed by making the bestuse of the computing power of the in-vehicle computer.

FIG. 2 is a schematic diagram showing a configuration of a vehiclecontrol system mounted on the vehicle 100. The vehicle control systemincludes an in-vehicle camera 110, a positioning information receiver120, a vehicle control device 130, a wireless terminal 140, anelectronic control unit (ECU) 150, an environmental informationacquisition sensor 160, and a driving state information acquisitionsensor 170. The in-vehicle camera 110, the positioning informationreceiver 120, the vehicle control device 130, the wireless terminal 140,the ECU 150, the environmental information acquisition sensor 160, andthe driving state information acquisition sensor 170 are connected sothat communication is possible via an in-vehicle network that complieswith standards such as controller area network (CAN) and Ethernet(registered trademark). In the present embodiment, each vehicle 100 hasthe same configuration regarding transmission of the learning data setto the server 200 and vehicle control by applying the learned model, andsince the server 200 applies the same processes for each vehicle 100,one vehicle 100 will be described below unless otherwise necessary.

The in-vehicle camera 110 has a two-dimensional detector composed of anarray of photoelectric conversion elements having sensitivity to visiblelight, such as a charge-coupled device (CCD) or a complementarymetal-oxide-semiconductor (C-MOS), and an imaging optical system thatforms an image of the region to be imaged on the two-dimensionaldetector. The in-vehicle camera 110 is provided on the dashboard insidethe vehicle, near the windshield, or the like, and captures images ofsurroundings of the vehicle 100 (for example, in front of the vehicle100) every predetermined imaging cycle (for example, 1/30 second to 1/10second) to generate images showing the surroundings of the vehicle 100.The images obtained by the in-vehicle camera 110 may be color images.Further, the in-vehicle camera 110 may be composed of a stereo camera,or may be configured to acquire the distance between the in-vehiclecamera 110 and each structure on the image from the parallax of theright and left images. Each time the in-vehicle camera 110 generates animage, the in-vehicle camera 110 outputs the generated image to the ECU150 via the in-vehicle network.

The positioning information receiver 120 acquires positioninginformation representing the current position and posture of the vehicle100. For example, the positioning information receiver 120 can be aGlobal Positioning System (GPS) receiver. Each time the positioninginformation receiver 120 receives the positioning information, thepositioning information receiver 120 outputs the acquired positioninginformation to the ECU 150 via the in-vehicle network.

The vehicle control device 130 involves various devices related tovehicle control including devices such as a drive device including aninternal combustion engine or an electric motor serving as a drivesource for driving the vehicle 100, a braking device for braking thevehicle 100, a steering device for turning the vehicle 100, and thelike. When the drive source for driving the vehicle 100 is an electricmotor, the vehicle control device 130 may include a battery for storingelectric power, a fuel cell for supplying electric power to the electricmotor, and the like. In the present embodiment, vehicle control is aconcept that includes general control related to the vehicle 100 inaddition to directly controlling the vehicle 100 by the above devices,and includes, for example, control that is indirectly related to drivingthe vehicle 100 such as control of an air conditioner, a display device,and a sound device.

The wireless terminal 140 includes, for example, an antenna and a signalprocessing circuit that executes various processes related to wirelesscommunication such as modulation and demodulation of wireless signals.The wireless terminal 140 receives downlink wireless signals from theradio base station 400, and also transmits uplink wireless signals tothe radio base station 400. That is, the wireless terminal 140 extractsa signal (for example, a learned model) transmitted from the server 200to the vehicle 100 from the downlink wireless signals received from theradio base station 400 and passes the signal to the ECU 150. Further,the wireless terminal 140 generates an uplink wireless signal includinga signal (for example, a learning data set, specification information,etc.) to be transmitted to the server 200, which is received from theECU 150, and transmits the wireless signal.

The ECU 150 is a mode of a control device for the vehicle, and includesa processor 152, a memory 154, and a communication interface 156. Theprocessor 152 has one or more central processing units (CPUs) andperipheral circuits thereof. The processor 152 may further include otherarithmetic circuits such as a logical operation unit, a numericaloperation unit, or a graphic processing unit. The memory 154 includes,for example, a volatile semiconductor memory and a non-volatilesemiconductor memory. The memory 154 stores various types of informationsuch as the specification information of the ECU 150 and the learnedmodel provided by the server 200. The communication interface 156 has aninterface circuit for connecting the ECU 150 to the in-vehicle network.

The environmental information acquisition sensor 160 is a sensor thatmainly acquires information representing the environment around thevehicle 100 (hereinafter, also referred to as environmentalinformation). The environmental information acquisition sensor 160includes an outside air temperature sensor, an illuminance sensor thatdetects the illuminance outside the vehicle 100, a rainfall sensor thatdetects the amount of rainfall outside the vehicle 100, a LightDetection and Ranging (LiDAR) sensor, and the like. The environmentalinformation acquisition sensor 160 outputs the acquired environmentalinformation to the ECU 150 via the in-vehicle network.

The driving state information acquisition sensor 170 is a sensor thatacquires various types of information related to the driving state ofthe vehicle 100 (hereinafter, also referred to as driving stateinformation), and includes an accelerator operation amount sensor thatdetects the operation amount of the accelerator pedal, a sensor thatdetects depressing of the brake pedal (brake hydraulic sensor), asteering angle sensor that detects the steering angle of the steeringwheel, a vehicle speed sensor that detects the vehicle speed, anacceleration sensor, a gyro sensor, and the like.

When the vehicle 100 employs an internal combustion engine as a drivesource, the driving state information acquisition sensor 170 may includevarious sensors that detect the operating state of the internalcombustion engine such as an intake air amount sensor (air flow meter),a pressure sensor and a temperature sensor that detect the pressure andthe temperature of the intake air, an exhaust temperature sensor, anair-fuel ratio sensor that detects the air-fuel ratio of the exhaustgas, a hydrocarbon (HC) concentration sensor that detects the HCconcentration in the exhaust gas, a carbon monoxide (CO) concentrationsensor that detects the CO concentration in the exhaust gas, atemperature sensor that detects the temperature of the exhaust reductioncatalyst, a crank angle sensor that detects the rotation angle of thecrank shaft, and a coolant temperature sensor that detects the coolanttemperature of the internal combustion engine.

Further, when the vehicle 100 employs an electric motor as a drivesource, the driving state information acquisition sensor 170 may includevarious sensors that detect the operating state of the electric motor,such as a sensor that detects the current value and the voltage value ofthe electric motor. Further, when the electric motor is driven by theelectric power generated by the fuel cell, the driving state informationacquisition sensor 170 may include various sensors that detect theoperating state of the fuel cell, such as a voltage sensor that detectsthe cell voltage of the fuel cell, a pressure sensor that detects theanode gas pressure, and a sensor that detects the cathode gas flow rate.

FIG. 3 is a schematic diagram showing a configuration of the server 200.The server 200 has a control device 210 and a storage device 220.

The control device 210 includes a processor 212, a memory 214, and acommunication interface 216. The processor 212 has one or more CPUs andperipheral circuits thereof. The processor 212 may further include otherarithmetic circuits such as a logical operation unit, a numericaloperation unit, or a graphic processing unit. The memory 214 includes,for example, a volatile semiconductor memory and a non-volatilesemiconductor memory. The communication interface 216 has an interfacecircuit for connecting the control device 210 to the network in theserver 200 or the communication network 300. The communication interface216 is configured to be communicable with the vehicle 100 via thecommunication network 300 and the radio base station 400. That is, thecommunication interface 216 passes the learning data set, thespecification information, and the like received from the vehicle 100via the radio base station 400 and the communication network 300 to theprocessor 212. Further, the communication interface 216 transmits thelearned model received from the processor 212 to the vehicle 100 via thecommunication network 300 and the radio base station 400.

The storage device 220 includes, for example, a hard disk device or anoptical recording medium and an access device thereof. The storagedevice 220 stores a plurality of learned models of different scalesacquired by performing learning by the server 200 using the learningdata set. Further, the storage device 220 stores the learning data settransmitted from the vehicle 100 as necessary. Further, as will bedescribed later, the storage device 220 stores the map information, andstores the learned models in association with each region on the mapinformation. The storage device 220 may store a computer program forexecuting a process executed on the processor 212.

FIG. 4 is a schematic diagram showing both functional blocks of theprocessor 152 of the ECU 150 provided in the vehicle 100 and functionalblocks of the processor 212 of the control device 210 provided in theserver 200.

The processor 152 of the ECU 150 includes a data acquisition unit 152 a,a data set creation unit 152 b, a specification information acquisitionunit 152 c, a vehicle control unit 152 d, a transmission unit 152 e, anda reception unit 152 f. Each of these units of the processor 152 is, forexample, a functional module realized by a computer program that runs onthe processor 152. That is, each of these units of the processor 152 iscomposed of the processor 152 and a program (software) for operating theprocessor 152. Further, the program may be recorded in the memory 154included in the ECU 150 or a recording medium connected from theoutside. Alternatively, each of these units of the processor 152 may bea dedicated arithmetic circuit provided in the processor 152.

The data acquisition unit 152 a of the processor 152 acquires the imagegenerated by the in-vehicle camera 110, the positioning informationacquired by the positioning information receiver 120, the environmentalinformation acquired by the environmental information acquisition sensor160, and the driving state information acquired by the driving stateinformation acquisition sensor 170, and the like.

Further, the data acquisition unit 152 a acquires various data based onthe image generated by the in-vehicle camera 110, the environmentalinformation, or the driving state information. For example, the dataacquisition unit 152 a recognizes the image generated every imagingcycle described above by the in-vehicle camera 110 (includingrecognition by machine learning) to acquire determination values ofinformation such as the road surface condition of the road surface onwhich the vehicle 100 travels, structures around the vehicle 100,another vehicle traveling around the vehicle 100, or a weathercondition. In addition, the data acquisition unit 152 a acquires pointcloud data indicating an object existing around the vehicle 100 byprocessing the information acquired by scanning the LiDAR. Further, thedata acquisition unit 152 a acquires various data by processing thedriving state information, for example, by calculating the engine speedbased on the output signal of the crank angle sensor. In this way, thedata acquisition unit 152 a can acquire various data based on the image,the environmental information, or the driving state information byperforming predetermined processes on the image, the environmentalinformation, or the driving state information.

Further, the data acquisition unit 152 a acquires control values(command values) to be used by the ECU 150 when the ECU 150 controls thevehicle. When the vehicle 100 employs an internal combustion engine as adrive source, these control values may include, for example, ignitiontiming, fuel injection amount, fuel injection timing, variable valvetiming (VVT), the control amount of the exhaust gas recirculation (EGR)valve for adjusting the gas flow rate of the EGR device, and the like.Further, when the vehicle 100 employs an electric motor as a drivesource, these control values may include, for example, a current value,a voltage value, and the like of the electric motor. When the electricmotor is driven by the electric power generated by the fuel cell, thesecontrol values may include the current value supplied from the fuel cellto the electric motor, the unit cell voltage, the anode gas pressure,the cathode gas flow rate, the number of revolutions of the compressorthat compresses the cathode gas, the instruction value for controllingvarious valves provided in the flow path of the anode gas or the cathodegas, and the like. Further, these control values may include controlvalues for systems that support the driving of drivers such as anti-lockbraking system (ABS), vehicle stability control system (VSC), andtraction control system (TRC). Further, these control values may includecontrol values that are indirectly related to vehicle control, such asthe control value indicating the operating state of the wiper of thevehicle 100, the set value of the instruction signal of the headlamp(the set value indicating either the high beam or the low beam, and thelike), and the set value for controlling the air conditioner in thevehicle cabin (the set value indicating air volume, set temperature,mode, and the like). In the following, these control values used by theECU 150 when the ECU 150 controls the vehicle 100 will be referred to as“the control value of the ECU 150”.

It should be noted that the data acquisition unit 152 a does not need toacquire all of the above-mentioned information, and the data acquisitionunit 152 a may acquire only the information necessary for creating thedata set in accordance with the learning data set created by the dataset creation unit 152 b.

The data set creation unit 152 b of the processor 152 creates a learningdata set to be used for learning of the server 200 by combining variousdata acquired by the data acquisition unit 152 a.

The data set created by the data set creation unit 152 b differsdepending on the content to be learned on the server 200 side. Forexample, when predicting the temperature of the exhaust reductioncatalyst as in JP 2019-183698 A described above, a data set is createdmainly based on the driving state information. Further, for example,when learning is performed by associating the environment around theroad on which the vehicle 100 travels with the driving state of thevehicle 100, a data set may be created based on the environmentalinformation, the driving state information, and the control value of theECU 150. Further, for example, when learning is performed by associatingthe image generated by the in-vehicle camera 110 with the driving stateof the vehicle 100, a data set may be created based on the imagegenerated by the in-vehicle camera 110, the driving state information,and the control value of the ECU 150. An example of the learning dataset will be described later.

The specification information acquisition unit 152 c of the processor152 acquires the specification information of the in-vehicle computerprovided in the vehicle 100, that is, the specification information ofthe ECU 150. The specification information of the ECU 150 is stored inadvance in the memory 154 of the ECU 150. The specification informationis information representing the computing power of the ECU 150,including, for example, the clock frequency of the processor 152, thenumber of cores, the capacity of the memory 154, and the like.

The vehicle control unit 152 d of the processor 152 controls the vehicle100 by performing processes to which the learned model transmitted fromthe server 200 is applied. For example, the vehicle control unit 152 dinputs various data based on the image of the in-vehicle camera 110, theenvironmental information, the driving state information, or the controlvalue of the ECU 150 into the learned model as input values to acquirean output value from the learned model, thereby performing vehiclecontrol based on the output value.

The transmission unit 152 e of the processor 152 is one mode of acomputing power information transmission unit, and transmits thespecification information acquired by the specification informationacquisition unit 152 c to the server 200. Further, the transmission unit152 e is one mode of a learning data set transmission unit, andtransmits the learning data set created by the data set creation unit152 b to the server 200. The reception unit 152 f of the processor 152is one mode of a learned model reception unit, and receives the learnedmodel of a scale corresponding to the computing power of the ECU 150from the server 200.

The processor 212 of the control device 210 provided in the server 200includes a model scale determination unit 212 a, a learning unit 212 b,a transmission unit 212 c, and a reception unit 212 d. Each of theseunits of the processor 212 is, for example, a functional module realizedby a computer program that runs on the processor 212. That is, each ofthese units of the processor 212 is composed of the processor 212 and aprogram (software) for operating the processor 212. Further, the programmay be recorded in the memory 214 included in the control device 210,the storage device 220, or a recording medium connected from theoutside. Alternatively, each of these units included in the processor212 may be a dedicated arithmetic circuit provided in the processor 212.

The model scale determination unit 212 a of the processor 212 determinesthe scale of the learned model based on the specification informationtransmitted from the ECU 150 of the vehicle 100. For example, the modelscale determination unit 212 a determines the scale of the learned modelbased on a map in which the relationship between the computing power ofthe ECU 150 of the vehicle 100 represented by the specificationinformation and the scale of the learned model is defined in advance.

FIG. 5 is a schematic diagram showing an example of a map defining inadvance the relationship between the computing power of the ECU 150 andthe scale of the learned model. In FIG. 5, both the computing power ofthe ECU 150 and the scale of the learned model are quantified and shown.The larger the numerical value is, the higher the computing power of theECU 150 is and the larger the scale of the learned model is. In the mapshown in FIG. 5, the relationship between the computing power of the ECU150 and the scale of the learned model is defined in advance so that thehigher the computing power of the ECU 150 is, the larger the scale ofthe learned model is. The model scale determination unit 212 adetermines the scale of the learned model by applying the computingpower of the ECU 150 obtained from the specification information to themap of FIG. 5.

FIG. 6 is a schematic diagram showing an example of a table used by themodel scale determination unit 212 a to obtain the computing power fromthe specification information of the ECU 150. As shown in FIG. 6, thecomputing power of the ECU 150 is quantified based on the clockfrequency of the processor 152, the number of cores, and the capacity ofthe memory 154. The higher the numerical values of the clock frequency,the number of cores, and the memory capacity are, the higher thenumerical value of the computing power is, and thus the higher thecomputing power is. In the example shown in FIG. 6, as an example, thecomputing power is a value obtained by multiplying the clock frequency,the number of cores, and the memory capacity, which is represented bythe following formula: (computing power)=(clock frequency)×(number ofcores)×(memory capacity). In the four types of examples of the ECU 150(ECU_A, ECU_B, ECU_C, and ECU_D) shown in FIG. 6, the computing power ofthe ECU_C, which has the maximum clock frequency, the number of cores,and the memory capacity, is the highest. The numerical values of theclock frequency, the number of cores, and the memory capacity shown inFIG. 6 are examples.

For example, when the specification information transmitted from acertain vehicle 100 corresponds to the ECU_A shown in FIG. 6, the modelscale determination unit 212 a determines from the table of FIG. 6 thatthe computing power of the ECU 150 of the vehicle 100 is “72”. Then, themodel scale determination unit 212 a determines that the scale of thelearned model corresponding to the computing power of the ECU 150 is,for example, “260” based on the map of FIG. 5. Therefore, when a learnedmodel of a scale of “260” or less is provided, the vehicle 100 canperform vehicle control by applying the learned model. The numericalvalue of the computing power of the ECU 150, the numerical value of thescale of the learned model, and the relationship therebetween shown inFIG. 5 are examples, and the present embodiment is not limited thereto.

Instead of the table shown in FIG. 6, the computing power may becalculated based on a map or a function that defines the relationshipbetween the computing power and parameters such as the clock frequency,the number of cores, and the memory capacity. FIG. 6 shows an example inwhich the computing power of the ECU 150 can be obtained based on theclock frequency, the number of cores, and the memory capacity.Alternatively, for example, the computing power of the ECU 150 can beobtained based on, for example, other specifications such as the amountof data that can be transmitted by the communication interface 156 inaddition to the above parameters.

The learning unit 212 b of the processor 212 uses a neural network inaccordance with the scale of the learned model determined by the modelscale determination unit 212 a, and performs learning based on thelearning data set transmitted from the vehicle 100. FIG. 7 is aschematic diagram showing an example of a neural network in which thelearning unit 212 b performs learning based on a learning data set andcreates a learned model. The neural network shown in FIG. 7 has the sameconfiguration as that described in JP 2019-183698 A, and since theneural network itself is already known, an outline will be describedhere. The circles in FIG. 7 represent artificial neurons, and in neuralnetworks, these artificial neurons are usually referred to as nodes orunits (here, referred to as nodes). In FIG. 7, L=1 indicates an inputlayer, L=2 and L=3 indicate hidden layers, and L=4 indicates an outputlayer. As shown in FIG. 7, the input layer (L=1) consists of five nodes,and the input values x₁, x₂, x₃, x₄, and x₅ are input to the nodes ofthe input layer (L=1). FIG. 7 schematically shows two hidden layers, ahidden layer (L=2) and a hidden layer (L=3), each having six nodes.However, the number of layers of these hidden layers may be one or anynumber, and the number of nodes existing in these hidden layers may alsobe any number. In the present embodiment, the number of hidden layersand the number of nodes in the hidden layers are determined according tothe scale of the learned model determined by the model scaledetermination unit 212 a. z₂₁, z₂₂, z₂₃, z₂₄, z₂₅, and z₂₆ indicate theoutput values from each node of the hidden layer (L=2), and z₃₁, z₃₂,z₃₃, z₃₄, z₃₅, and z₃₆ indicate the output values from each node of thehidden layer (L=3). The number of nodes in the output layer (L=4) isone, and the output value from the node in the output layer is indicatedby y.

At each node of the input layer (L=1), the input values x₁, x₂, x₃, x₄,and x₅ are output as they are. The output values x₁, x₂, x₃, x₄, and x₅of each node of the input layer are input to each node of the hiddenlayer (L=2), and in each node of the hidden layer (L=2), the total inputvalue u_(2k)=Σx.w+b(k=1 to 6) is calculated using the correspondingweight w and bias b.

Next, this total input value u_(2k) is converted by the activationfunction f, and is output as an output value z_(2k) (=f(u_(2k))) fromeach node represented by z_(2k) of the hidden layer (L=2). The outputvalues z₂₁, z₂₂, z₂₃, z₂₄, z₂₅, and z₂₆ of each node of the hidden layer(L=2) are input to each node of the hidden layer (L=3), and in each nodeof the hidden layer (L=3), the total input value u_(3k)=Σz.w+b(k=1 to 6)is calculated using the corresponding weight w and bias b. This totalinput value u_(3k) is similarly converted by the activation function andoutput as output values z₃₁, z₃₂, z₃₃, z₃₄, z₃₅, and z₃₆ from each nodeof the hidden layer (L=3). As the activation function, for example, asigmoid function σ(x)=1/(1+exp (−x)), a rectified linear function(ReLU)S(u)=max(0, u), or the like is used.

The output values z₃₁, z₃₂, z₃₃, z₃₄, z₃₅, and z₃₆ of each node of thehidden layer (L=3) are input to the node of the output layer (L=4). Inthe node of the output layer, the total input value u_(4k)=Σz.w+b iscalculated using the corresponding weight w and bias b, or the totalinput value u_(4k)=Σz.w is calculated using only the correspondingweight w. In this example, the identity function is used at the node ofthe output layer, and the total input value u_(4k) calculated in thenode of the output layer is output as it is as the output value y fromthe node of the output layer. The number of nodes in the output layermay be two or more.

One data set created by the data set creation unit 152 b includes inputvalues x₁, x₂, x₃, x₄, and x₅ and teacher data y_(t) for the inputvalues x₁, x₂, x₃, x₄, and x₅. In the case where the teacher data y_(t)is obtained for a certain input value, and the output value from theoutput layer (L=4) for this input value is y, when the square error isused as an error function, the square error E is obtained byE=(½).(y−y_(t))².

The learning unit 212 b inputs the input values included in the data settransmitted from the vehicle 100 to the neural network, and calculatesthe square error E from the obtained output value y and the teacher datay_(t) included in the data set. Then, in order to minimize the sum ofthe square errors E obtained from the plurality of learning data sets,the learning unit 212 b performs operations such as an error backpropagation method and a stochastic gradient descent method to calculatethe weight w and the bias b for each node and create the learned model.When the teacher data cannot be detected on the vehicle 100 side, thelearning unit 212 b may create a learned model by unsupervised learningor reinforcement learning.

At this time, the learning unit 212 b prepares a plurality of neuralnetworks having a different number of hidden layers and a differentnumber of nodes existing in the hidden layers, and performs learningwith each neural network to create learned models of different scales.As a result, even when a plurality of learning data sets are the same,learned models of different scales can be obtained. The learned modelscreated by the learning unit 212 b are transmitted to the vehicle 100.Further, the learned models created by the learning unit 212 b arestored in the storage device 220. Note that FIG. 3 shows a case wherethe learned models of different scales are accumulated in the storagedevice 220. The values of the scale of the learned models shown in FIG.3 are examples, and the present embodiment is not limited thereto.

The transmission unit 212 c of the processor 212 is one mode of alearned model transmission unit, and transmits the learned model createdby the learning unit 212 b to the vehicle 100. Specifically, thetransmission unit 212 c transmits information required for performingvehicle control to which the ECU 150 of the vehicle 100 applies thelearned model, such as the values of the weight w and bias b of eachnode of the neural network of the learned model, and the configurationof the neural network (the number of layers, the number of nodes, etc.).At this time, the transmission unit 212 c transmits a learned model of alarger scale for a vehicle 100 equipped with an ECU 150 having a highercomputing power.

The reception unit 212 d of the processor 212 is one mode of a learningdata set reception unit, and receives the learning data set transmittedfrom the ECU 150 of the vehicle 100. Further, the reception unit 212 dis one mode of a computing power information reception unit, andreceives information on the computing power of the ECU 150 of thevehicle 100.

In the configuration shown in FIG. 4, the processes performed by thedata acquisition unit 152 a and the data set creation unit 152 b of theprocessor 152 of the ECU 150 may be performed by the processor 212 ofthe control device 210 of the server 200. In this case, raw data such asthe image generated by the in-vehicle camera 110, the positioninginformation acquired by the positioning information receiver 120, theenvironmental information acquired by the environmental informationacquisition sensor 160, and the driving state information acquired bythe driving state information acquisition sensor 170 is transmitted tothe server 200, and the processor 212 of the control device 210 on theserver 200 side performs the processes instead of the data acquisitionunit 152 a and the data set creation unit 152 b.

Next, the processes performed by the ECU 150 provided in the vehicle 100and the control device 210 provided in the server 200 will be describedwith reference to the sequence diagram of FIG. 8. In FIG. 8, theprocesses on the vehicle 100 side are performed by the ECU 150 providedin the vehicle 100 at predetermined control cycles. Further, theprocesses on the server 200 side are performed by the control device 210provided in the server 200 at predetermined control cycles.

First, on the vehicle 100 side, the data set creation unit 152 b of theprocessor 152 creates a learning data set from the data acquired by thedata acquisition unit 152 a (step S10). Next, the data set creation unit152 b determines whether a predetermined amount or more of the data setis accumulated (step S11). When a predetermined amount or more of thedata set is accumulated in step S11, the specification informationacquisition unit 152 c acquires the specification information, and thespecification information and the learning data set are combined tocreate transmission data (step S12). On the other hand, when apredetermined amount or more of the data set is not accumulated in stepS11, the processes in this control cycle end. In addition to thespecification information and the learning data set, the transmissiondata may include vehicle identification information (vehicle ID) foridentifying each vehicle 100, the positioning information acquired bythe positioning information receiver 120, and the like. In particular,when a learned model is created for each region in which the vehicle 100travels as described later, the transmission data may include thepositioning information. Next, the transmission unit 152 e of theprocessor 152 transmits the transmission data to the server 200 (stepS14).

When the server 200 performs learning based on the learning data setscreated by a plurality of vehicles 100, the learning data sets are alsotransmitted from other vehicles 100 to the server 200, so thetransmission data may be created without performing the process of stepS11. On the other hand, when the server 200 performs learning based onlyon the learning data set created by one specific vehicle 100, apredetermined amount or more of the data set is required for the server200 to perform the learning, so the process of S11 may be performed.

Further, when the server 200 performs learning based on the learningdata sets created by a plurality of vehicles 100, the server 200 canperform learning based on the learning data sets acquired from the othervehicles 100. Therefore, in this case, the transmission data created instep S12 need not include the learning data set.

On the server 200 side, it is determined whether the reception unit 212d of the processor 212 has received the transmission data transmittedfrom the vehicle 100 (step S20), and when the transmission data isreceived, the model scale determination unit 212 a determines the scaleof the learned model based on the specification information transmittedfrom the ECU 150 of the vehicle 100 (step S21). The model scaledetermination unit 212 a applies the specification information includedin the transmission data received from the vehicle 100 side to the tableshown in FIG. 6, or a map or function that defines the relationshipbetween the computing power and the parameters such as the clockfrequency, the number of cores, and the memory capacity to obtain thecomputing power of the ECU 150 of the vehicle 100. In the model scaledetermination unit 212 a, when the vehicle identification informationand the type of the ECU 150 (for example, the types such as the ECU_A tothe ECU_D shown in FIG. 6) are associated in advance and the type of theECU 150 can be determined from the vehicle identification information,the computing power of the ECU 150 may be obtained by applying the typeof the ECU 150 obtained from the vehicle identification information tothe table of FIG. 6. Therefore, the vehicle identification informationis one mode of information regarding the computing power of the ECU 150.The model scale determination unit 212 a applies the obtained computingpower of the ECU 150 to the map of FIG. 5 to obtain the scale of thelearned model. When the transmission data is not received in step S20,the processes in this control cycle end.

After step S21, in step S22, the learned model corresponding to thescale determined in step S21 is acquired (step S22). More specifically,in step S22, the learning unit 212 b of the processor 212 performslearning using the learning data set to create a learned model of ascale corresponding to the computing power of the ECU 150.Alternatively, in step S22, a learned model of a scale corresponding tothe computing power of the ECU 150 is selected from the learned modelsthat have already been created and stored in the storage device 220.

When the learning unit 212 b creates a learned model of a scalecorresponding to the computing power of the ECU 150, the learning unit212 b learns the data set by a neural network having the number ofhidden layers and the number of nodes corresponding to the scale of thelearned model determined by the model scale determination unit 212 a instep S21 to create a learned model. As a result, a learned model of ascale corresponding to the computing power of the ECU 150 is acquired.

Further, when selecting a learned model of a scale corresponding to thecomputing power of the ECU 150 from the learned models stored in thestorage device 220, a learned model is selected corresponding to thescale determined by the model scale determination unit 212 a in step S21from the learned models that have been already created. When a learnedmodel is created for each region in which the vehicle 100 travels asdescribed later, a learned model for the region corresponding to theposition information of the vehicle 100 is selected from the learnedmodels that have already been created.

Next, the transmission unit 212 c of the processor 212 transmits thelearned model acquired in step S22 to the vehicle 100 side (step S24).When a learned model is created for each region in which the vehicle 100travels as described later, the transmission unit 212 c transmits alearned model corresponding to the region in which the vehicle 100currently travels based on the positioning information received from thevehicle 100. After step S24, the processes in this control cycle on theserver 200 side end.

In the vehicle 100, it is determined whether the reception unit 152 f ofthe processor 152 has received the learned model (step S16), and whenthe learned model is received, the vehicle control unit 152 d of theprocessor 152 applies the learned model to perform vehicle control (stepS18). When the learned model has not been received in step S16, theprocess waits in step S16. After step S18, the processes in this controlcycle on the vehicle 100 side end.

When the reception unit 152 f of the processor 152 receives the learnedmodel in step S16, the received learned model is stored in the memory154. At this time, when a learned model is already stored in the memory154, the newly received learned model updates the learned model that hasbeen already stored in the memory 154.

Here, in the present embodiment, updating the learned model not onlyincludes the concept of updating the values of the weight and bias ofthe learned model, but also includes the concept of changing the numberof hidden layers or the number of nodes in the hidden layers of thelearned model that has already been stored in the memory 154. That is,updating the learned model includes the concept of changing theconfiguration of the learned model itself. In other words, the server200 can provide the vehicle 100 with a learned model having a differentnumber of hidden layers or a different number of nodes in the hiddenlayers from those of the existing learned model that has already beenstored in the memory 154. Further, the server 200 does not need toprovide the vehicle 100 with a learned model of the same scale as thatof the existing learned model that has already been stored in the memory154, and can provide the vehicle 100 with a learned model of a scaledifferent from that of the existing learned model within a range thatdoes not exceed the computing power of the ECU 150.

As described above, in the processes of FIG. 8, the processes of stepS10 and step S11 may be performed by the control device 210 of theserver 200.

Next, a specific example in which the scale of the learned model differsdepending on the specification information of the vehicle 100 will bedescribed. Here, as an example, a case where a learned model is createdfor each region in which the vehicle 100 travels and the scale of thelearned model differs depending on the size of the region will bedescribed. FIG. 9 is a schematic diagram showing an example in which alearned model is created for each region in which the vehicle 100travels. In FIG. 9, a certain range on the map is divided into eightregions, and learned models 1 to 8 are created for each of the eightregions 1 to 8.

The map information shown in FIG. 9 is stored in the storage device 220shown in FIG. 3 so as to be searchable. The map information is dividedinto each region shown in FIG. 9, and a learned model is assigned toeach region. In the storage device 220, the map information of theseregions and the assigned learned models are stored in association witheach other. More specifically, in the map information, the regions onthe map are divided based on the latitude and longitude to set eachregion, and a learned model is created for each region and associatedwith each region.

The learned model corresponding to the region 2 is created by performinglearning using a data set created based on the data collected by thevehicle 100 traveling in the region 2. When the vehicle 100 transmitsthe learning data set to the server 200, the vehicle 100 transmits thepositioning information indicating the position where the data set iscreated, that is, the position where various data included in the dataset is collected, to the server 200. As a result, the learning unit 212b of the processor 212 of the control device 210 can determine that theposition where the data set is created belongs to the region 2 based onthe position information included in the positioning information, sothat the data set corresponding to the region 2 can be used to performlearning to create a learned model corresponding to the region 2.Similarly, the server 200 performs learning on the other regions usingthe data set corresponding to each region to create a learned modelcorresponding to each region. The learned models created in this way arestored in the storage device 220 in association with each region on themap information.

The transmission unit 212 c of the processor 212 transmits the learnedmodels corresponding to each region to the vehicle 100 traveling in thecorresponding region based on the position information of the vehicle100. For example, a learned model 2 corresponding to the region 2 istransmitted to the vehicle 100 traveling in the region 2, and a learnedmodel 3 corresponding to a region 3 is transmitted to the vehicle 100traveling in the region 3.

By acquiring the learning data sets from the vehicle 100 traveling inregions 1 to 8 by the above method, the server 200 can create learnedmodels 1 to 8 corresponding to each of the regions 1 to 8. Further, theserver 200 can provide a learned model corresponding to the region inwhich the vehicle 100 travels to each of the vehicles 100 traveling ineach of the regions 1 to 8.

In FIG. 9, the thick arrow indicates the traveling route of a vehicle100. The vehicle 100 travels in the order of the region 2, the region 3,region 7, and region 8. When the vehicle 100 moves from the region 2 tothe region 3, the vehicle 100 sequentially receives the learned model 2and the learned model 3 from the server 200 in each region, andsequentially updates the learned model stored in the memory 154.

While the vehicle 100 is moving in the region 2, the learned modelsuitable for vehicle control is the learned model 2. The learned model 2is stored in the memory 154, and vehicle control is executed using thelearned model 2. After that, when the vehicle 100 moves from the region2 to the region 3, the server 200 extracts the learned model 3corresponding to the region 3 from the storage device 220 based on theposition information indicating that the current position of the vehicle100 is in the region 3, and transmits the learned model 3 to the vehicle100. The vehicle 100 receives the learned model 3 from the server 200and updates the learned model in the memory 154. The vehicle 100executes vehicle control using the learned model 3 while traveling inthe region 3. After that, the same applies when the vehicle 100 movesfrom the region 3 to the region 7, and when the vehicle 100 moves fromthe region 7 to the region 8.

As shown in FIG. 9, various factors such as the density of roads, thenumber of intersections, and the width of roads are region-dependent andvary from region to region. In addition, factors such as the curvatureof the roads, the gradient of the roads, and the traffic amount of othervehicles are also region-dependent and differ from region to region.Since the vehicle 100 traveling in each region can acquire a learnedmodel for the region corresponding to the current traveling position,the learned model corresponding to the current position is applied toperform vehicle control, making it possible to perform optimum vehiclecontrol that suits the factors for each region.

As described above, the larger the scale of the learned model is, themore accurately the model represents the actual state, and thus it issuitable for more sophisticated and precise vehicle control. Even whenthe learned model is different for each region, the larger the scale ofthe learned model is, the more accurately the model represents theactual state in the region. Therefore, the vehicle control may beperformed by applying the learned model of a larger scale. On the otherhand, when a large-scale learned model is provided to all the vehicles100, the vehicle 100 equipped with the ECU 150 having high computingpower can perform vehicle control by applying the large-scale learnedmodel, but the vehicle 100 equipped with the ECU 150 having relativelylow computing power cannot perform vehicle control by applying alarge-scale learned model.

Therefore, the learning unit 212 b of the processor 212 of the controldevice 210 creates a learned model for each region in which the vehicle100 travels, and a learned model of a larger scale is created as theregion is larger. The transmission unit 212 c of the processor 212transmits the learned model corresponding to each region in which thevehicle 100 currently travels based on the position information of thevehicle 100. Further, the reception unit 152 f of the ECU 150 receives alearned model corresponding to the region in which the vehicle 100currently travels for each region in which the vehicle 100 travels, anda learned model of a larger scale is received as the region is larger.

When the size of the region is small, the amount of information outputby the learned model is small in accordance with the size of the region.Therefore, even when the scale of the learned model is reduced, adecrease in the accuracy of vehicle control by applying the learnedmodel is suppressed. Therefore, by creating a small-scale learned modelcorresponding to a narrower region and providing the model to thevehicle 100, it is possible to perform desired vehicle control byapplying the learned model even when the vehicle 100 is equipped with anECU 150 having relatively low computing power. Note that the smaller theregion is, the smaller the number of learning data sets is used tocreate a learned model.

FIG. 10 shows an example in which the same range on the map as in FIG. 9is divided into 32 regions 11 to 42. In the example of FIG. 10, as inFIG. 9, learned models 11 to 42 corresponding to each region 11 to 42are created. As shown in FIG. 10, by increasing the number of divisionsof the regions and narrowing a region corresponding to one learnedmodel, it is possible to make a smaller-scale learned model correspondto each region as compared with FIG. 9.

In the present embodiment, since the learned model of the scalecorresponding to the computing power of the ECU 150 is transmitted fromthe server 200 to the vehicle 100 based on the specification informationof the vehicle 100, a small-scale learned model corresponding to arelatively small region as shown in FIG. 10 is transmitted to thevehicle 100 equipped with the ECU 150 having relatively low computingpower. The vehicle 100 to which the small-scale learned model istransmitted receives the learned model corresponding to the new regionfrom the server 200 each time the traveling region is switched, andperforms vehicle control by applying the learned model corresponding tothe new region. As the size of the region becomes narrower, thetraveling region is switched more frequently. Therefore, thetransmission unit 212 c of the processor 212 of the control device 210transmits a learned model to the vehicle 100 equipped with the ECU 150having low computing power at a higher transmission frequency than tothe vehicle 100 equipped with the ECU 150 having high computing power.Further, the reception unit 152 f of the processor 152 of the ECU 150receives a learned model with a higher reception frequency as thecomputing power of the ECU 150 is lower.

As a result, the vehicle 100 equipped with the ECU 150 having relativelylow computing power increases the update frequency of the learned modeleven when a small-scale learned model is used, so that a decrease in theaccuracy of the information output by the learned model is suppressed.Thus, it is possible to perform desired vehicle control by applying thelearned model. Further, in this case, since the small-scale learnedmodel is transmitted to the vehicle 100, the amount of traffic whentransmitting the learned model is suppressed.

Further, for the vehicle 100 equipped with the ECU 150 having relativelyhigh computing power, a large-scale learned model corresponding to arelatively wide region as shown in FIG. 9 is transmitted. The vehicle100 to which the large-scale learned model is transmitted receives thelearned model corresponding to the new region each time the travelingregion is switched, and performs vehicle control by applying the learnedmodel corresponding to the new region. At this time, since the learnedmodel corresponds to a relatively wide region, the switching of thetraveling region does not occur frequently. Therefore, the vehicle 100equipped with the ECU 150 having relatively high computing power canperform vehicle control with reduced update frequency of the learnedmodel.

As described above, even in the vehicle 100 equipped with the ECU 150having low computing power, a small-scale learned model is received fromthe server 200 every time the region is switched during traveling, andprocesses are performed while switching the learned models. Thus, it ispossible to perform desired vehicle control by applying the learnedmodel by making the best use of the computing power of the ECU 150.

Further, in the vehicle 100 equipped with the ECU 150 having highcomputing power, highly accurate vehicle control can be performed byapplying a large-scale learned model corresponding to a wide region.Further, in the vehicle 100 equipped with the ECU 150 having highcomputing power, the update frequency of the learned model is low, andthus the processes for updating the learned model are reduced.

Next, a learning data set and an example of a learned model will bedescribed. As described above, the data set creation unit 152 b of theprocessor 152 of the vehicle 100 creates a learning data set to be usedfor learning of the server 200 by combining various data acquired by thedata acquisition unit 152 a. The learning data set created by the dataset creation unit 152 b differs depending on the content to be learnedon the server 200 side.

An example of the learned model includes a learned model using ignitiontiming, fuel injection amount, injection timing, throttle openingdegree, variable valve timing, control amount of EGR valve that adjuststhe gas flow rate of the exhaust gas recirculation device, positioninformation of the vehicle 100, and weather information as the inputvalues and “NOx emission amount” as the output value y. In this case,the learning data set consists of the fuel injection amount, theinjection timing, the throttle opening degree, the variable valvetiming, the control amount of the EGR valve that adjusts the gas flowrate of the exhaust gas recirculation device, the position informationof the vehicle 100, and the weather information, and the NOx emissionamount as the teacher data y_(t). By performing processes applying thislearned model, the ECU 150 of the vehicle 100 can calculate the NOxemission amount, which is an output value, based on the input valuesthat change from moment to moment. In particular, since the input valuesinclude the position information of the vehicle 100 and the weatherinformation, the ECU 150 can calculate the NOx emission amount based onthe position where the vehicle 100 travels and the weather. Therefore,vehicle control based on the NOx emission amount is realized with highaccuracy.

Further, as described with reference to FIG. 9, when a learned model iscreated for each region in which the vehicle 100 travels, the learnedmodel for the region corresponding to the position in which the vehicle100 travels is provided to the vehicle 100. As a result, the ECU 150 ofthe vehicle 100 applies a learned model specialized for the regioncorresponding to the position where the vehicle 100 travels to performprocesses, so that the NOx emission amount can be calculated moreaccurately, and vehicle control based on the NOx emission amount isrealized with higher accuracy.

Further, another example of the learned model includes a learned modelusing point cloud data indicating an object existing around the vehicle100 acquired by an image generated by the in-vehicle camera 110 or byscanning the LiDAR when the vehicle 100 travels on a certain movementpath as the input values and the driving state of the vehicle 100 or thecontrol value of the ECU 150 when the image or the point cloud data isgenerated as the output value. The driving state here includes, forexample, operating states such as the steering angle of the steeringwheel, the accelerator operation amount, and the brake hydraulicpressure. In this case, the learning data set consists of the image andthe point cloud data, and the driving state or the control value of theECU 150 as the teacher data y_(t). According to this learned model, theoperation and the control that are performed on the vehicle 100according to the situation around the vehicle 100 indicated by the imageor the point cloud data are learned. Therefore, since the operationamount for the vehicle 100 or the control value of the ECU 150 iscalculated as the output value based on the image or the point clouddata that is the input value, it is possible to perform autonomousdriving based on the image generated by the in-vehicle camera 110 andthe point cloud data obtained by scanning the LiDAR, particularly on thevehicle 100 that performs autonomous driving.

Further, as described with reference to FIG. 9, when a learned model iscreated for each region in which the vehicle 100 travels, the learnedmodel for the region corresponding to the position in which the vehicle100 travels is provided to the vehicle 100. As a result, the ECU 150 ofthe vehicle 100 applies a learned model specialized for the regioncorresponding to the position where the vehicle 100 travels to performprocesses. Therefore, the operation amount for the vehicle 100 or thecontrol value of the ECU 150 can be calculated more accurately based onthe image that is the input value, so that vehicle control based on theoperation amount or the control value of the ECU 150 can be realizedwith higher accuracy.

In the present embodiment, an example in which the server 200 performsmachine learning by a neural network is shown, but the presentembodiment is not limited thereto. Among machine learning, supervisedlearning includes not only neural networks but also various methods suchas random forest, support vector machine, and k-nearest neighbor method.These models are common in that they are algorithms that draw boundariesin the feature space stretched by feature vectors and efficiently finddecision boundaries. That is, when estimation can be performed by aneural network, machine learning can be used with other supervisedlearning models.

As described above, according to the present embodiment, the server 200provides the vehicle 100 with a learned model of a scale correspondingto the computing power of the ECU 150 of the vehicle 100. Therefore, itis possible to suppress the learned model from being unavailable on thevehicle 100 side due to the lack of the computing power of the ECU 150.

What is claimed is:
 1. A server comprising a processor configured to:receive a data set from a vehicle; create a plurality of learned modelsof different scales by performing machine learning using the data set;receive, from the vehicle, information on computing power of anelectronic control unit that controls the vehicle by applying thelearned model; and transmit the learned model to the vehicle, whereinthe processor is configured to transmit the learned model of a largerscale to the vehicle equipped with the electronic control unit havinghigh computing power, than to the vehicle equipped with the electroniccontrol unit having low computing power.
 2. The server according toclaim 1, wherein the processor is configured to perform the machinelearning by a neural network.
 3. The server according to claim 2,wherein the scale of the learned model is larger as the number of hiddenlayers or the number of nodes existing in the hidden layers increases.4. The server according to claim 1, wherein the processor is configuredto: create the learned model for each region in which the vehicletravels, the learned model being of a larger scale as the region islarger; and transmit the learned model corresponding to the region inwhich the vehicle currently travels based on position information of thevehicle.
 5. The server according to claim 4, wherein the processor isconfigured to transmit the learned model at a higher transmissionfrequency to the vehicle equipped with the electronic control unithaving low computing power, than to the vehicle equipped with theelectronic control unit having high computing power.
 6. A control deviceof a vehicle that controls the vehicle by applying a learned model, thecontrol device comprising a processor configured to: transmitinformation on computing power of the control device to a server;receive the learned model of a scale corresponding to the computingpower from the server; and control the vehicle by applying the learnedmodel of the scale corresponding to the computing power, the learnedmodel being received from the server.
 7. The control device according toclaim 6, wherein the processor is configured to: transmit a data set tothe server; and receive the learned model created by the server byperforming machine learning using the data set.
 8. The control deviceaccording to claim 7, wherein: the server performs the machine learningby a neural network; and the scale of the learned model is larger as thenumber of hidden layers or the number of nodes existing in the hiddenlayers increases.
 9. The control device according to claim 6, whereinthe processor is configured to: receive the learned model correspondingto a region in which the vehicle currently travels for each region inwhich the vehicle travels; and receive the learned model of a largerscale as the region is larger.
 10. The control device according to claim9, wherein the processor is configured to receive the learned model at ahigher reception frequency as the computing power is lower.
 11. Amachine learning system for a vehicle, the machine learning systemcomprising: a server that creates a learned model by performing learningusing a data set received from the vehicle; and a control device for thevehicle for controlling the vehicle by applying the learned model,wherein: the control device includes a first processor configured to:transmit the data set to the server; transmit information on computingpower of the control device to the server; receive the learned model ofa scale corresponding to the computing power from the server; andcontrol the vehicle by applying the learned model of the scalecorresponding to the computing power, the learned model being receivedfrom the server; and the server includes a second processor configuredto: receive the data set from the vehicle; create a plurality of learnedmodels of different scales by performing machine learning using the dataset; receive information on the computing power of the control devicefrom the vehicle; transmit the learned model to the vehicle; andtransmit the learned model of a larger scale to the vehicle equippedwith the control device having high computing power, than to the vehicleequipped with the control device having low computing power.
 12. Themachine learning system according to claim 11, wherein the secondprocessor is configured to perform the machine learning by a neuralnetwork.
 13. The machine learning system according to claim 12, whereinthe scale of the learned model is larger as the number of hidden layersor the number of nodes existing in the hidden layers increases.
 14. Themachine learning system according to claim 11, wherein: the secondprocessor is configured to: create the learned model for each region inwhich the vehicle travels; create the learned model of a larger scale asthe region is larger; and transmit the learned model corresponding tothe region in which the vehicle currently travels based on positioninformation of the vehicle; and the first processor is configured toreceive the learned model corresponding to the region in which thevehicle currently travels for each region in which the vehicle travels.15. The machine learning system according to claim 14, wherein: thesecond processor is configured to transmit the learned model at a highertransmission frequency to the vehicle equipped with the control devicehaving low computing power, than to the vehicle equipped with thecontrol device having high computing power; and the first processor isconfigured to receive the learned model at a higher reception frequencyas the computing power is lower.